Global navigation satellite systems (GNSS) are already widely known as state of the art technology. They have in common that a large number of satellites in earth orbit send signals which are normally modulated to a carrier frequency and describe the position of the satellite and a broadcast time of the so-called code signal. Via the code signal with the position of the satellite and the time of the transmission, a three-dimensional position on the earth can basically be determined just from the satellite signals of three satellites if the clocks in a receiver are perfectly synchronized with those of the satellites, after which a distance (run-time measurement) results from the run-time of the satellite signals, and the points of intersection on the spherical surfaces that result from the distance between the receiver and the satellite are located. Since a clock error is usually present, the satellite signals from four satellites are required in order to be able to determine a three-dimensional position on the earth using a run-time measurement, since there are then four equations for four unknowns. Signals from other satellites are frequently used for plausibility checks and the like. Methods were also proposed that work with fewer than four satellite signals, these then work with movement models and/or temporal correlations.
In the method described here, the position is determined using the run time of the satellite signal from the satellite to the receiver, from which the distance between the satellite and the receiver is derived. This resulting distance together with the receiver clock error is usually designated as the “pseudo-range”; the run-time measurement is often designated as the “code measurement” “code-phase measurement” since it evaluates the code modulated on the carrier frequency of the satellite's signals.
Given a clear line of sight to the sky, simple GNSS receivers today achieve a precision of under 5 m 95% of the time. Because of the high speed of light, in most cases this still results in inaccuracies in the run-time measurement. Many modern GPS receivers therefore use yet another variable for distance measurements, namely the so-called carrier-phase measurement, which uses Doppler measurements in which the carrier frequency phase and its change is analyzed. While phase measurements in the determination of distances for other uses such as time-of-flight cameras are extremely common, there exists GNSS receivers the problem that it is unknown how many full wave trains there are between the satellite and the GNSS receiver. Therefore, there is always an ambiguity in a phase measurement since it is not known how many full wavelengths have already been completed. Consequently, the Doppler measurement (carrier-phase measurement) usually refers to a reference point from which the phase is most commonly observed and, thus, the number of wavelengths to this reference, at least, is known. Methods have also been proposed in which the number of complete wavelengths to the satellite is estimated in older to also determine a distance between the receiver and the satellite using the carrier-phase measurement. A problem can occur during a temporary loss of the satellite signal, since then a passage of at least one wavelength is, if applicable, missed, so that a loss of the phase relationship occurs. The causes of this can, for example, be signal blocking by obstructions, but also multipath effects and the like. Such a loss of the phase relationship is designated as a “loss of lock” and mostly results in a jump in the measurement of the carrier phase (“cycle slip”). The run-time measurement, however, is not influenced by this.
In many modern applications, particularly in motor vehicles, a very high localization accuracy is now required in the receiver, for example, in advanced driving-assistance systems in motor vehicles. In the context of a GNSS measurement, a plurality of errors occur, of which the “multipath error” caused by multipath effects is one. In built-up areas and, above all, on narrow streets or around high house facades, shadows and reflections often occur that can thus lead to large position errors. If a line-of-sight connection to the appropriate satellite still remains, the satellite signal received by direct line-of-sight (LOS) and multipath signals that are overlaid on it, reflected or slightly shifted are present, result in effects similar to a beat and like. Several methods have been proposed to suppress multipath effects using hard and/or software.
A first approach concerns the adjustment of parameters correlator provided on the input side of the receiver, wherein high bandwidths of the pre-correlator as well as narrow correlator distances increase the robustness compared to satellite signals bearing multipath errors; see, for example, the standard work by E. D. Kaplan and C. J. Hegarty, “Understanding GPS—Principles and Applications”, Norwood: Artech House 2005. A further proposed approach is the use of derivatives of RAIM (Receiver. Autonomous Integrity Monitoring) to recognize faulty satellite signals/distance measurements. A distinction is made between sequential and snapshot RAIM. With Snapshot RAIM, the simultaneous observations of different satellites are verified against each other. An observation whose residuum exceeds an appropriate threshold is discarded. With sequential RAIM, predictions from past measurements are used for the formation of residue; see, for example, the article by S. Hewitson and J. Wang, “Extended Receiver Autonomous Integrity Monitoring GNSS/INS Integration”, Journal of Surveying Engineering, Vol. 136, no. 1 pp. 13-22, 2010.
In addition to detection methods based on the pseudo range, thus on the run-time measurement, approaches also exist which use a receiver's additional output variables, for example, the result of the Doppler measurement. This exploits the fact that the multipath error affects the carrier-phase measurement and the run-time measurement differently. By subtracting the distance (Doppler distance) identified by the Doppler measurement (carrier-phase measurement) from the distance (run-time distance) identified by the run-time measurement based on modulated code, the so-called Code-Minus-Carrier (CMC) variable can be formed and the amount of the additional multipath error estimated, as is described, for example, article by M. S. Braasch, “Multipath effects”, in: Global Positioning System Theory and Applications, Vol. 1, pp. 547-568, 1996. The CMC variable or similar variables can also be analyzed in the frequency range and a multipath frequency characteristic for the geometry can be damped.
In general, it must be noted that the multipath error is a local phenomenon. Consequently, no correction on the basis of a reference infrastructure (for example, differential GPS) can be made. A receiver-autonomous detection method must therefore be employed. The state of the art methods in known to date, however, have some disadvantages.
The widespread, snapshot RAIM methods of error detection and identification require a minimum number of visible satellites. Detection of a multipath error is only possible with five satellite signals, while the identification of an erroreous observation can only take place if six satellite signals exist. Out of n observations, variations of the traditional RAIM can identify the n−5 faulty ones; see also G. Schrott, M. Rippl, A. Ene, J. Blanch, B. Belabbas, T. Walter, P. Enge and M. Meurerm, “Enhancements of the Range Consensus algorithm (RANCO)”, in: Proceedings of the ION GNSS Conference, 2008.
For many actually occurring applications, however, the high number of satellite signals needed is a significant restriction. It is precisely in urban areas, where multipath propagation is a major challenge, high house facades can block a large portion of the view to the sky. In such urban canyons, therefore, there is often not a sufficient number of satellites which can be received for detection of a satellite signal burdened with a multipath error.
For methods that are based on sequential RAIM, a status estimator is required to determine the predictions based on past measurements. This introduces additional complexity, and model and prediction errors are inevitable.
For static applications, the use of the CMC variable in particular has proven extremely beneficial; see again the aforementioned article by M. S. Baasch. The application of the method becomes problematic, however, in the case of a moving receiver, as is used in motor vehicles, for example. The previously described cycle slips then occur significantly more frequently, which complicates the carrier phase ambiguity estimation. However, a resolved carrier phase ambiguity resolution is required to produce the CMC variable. Even methods that work in the frequency range fail in the dynamic case, because a constantly changing geometry prevents the formation of a characteristic frequency in the observations.